Office Hours: M/F 3-5 or by appointment (in-person or Zoom)
"We should have aggressive and wild ambitions that are only anchored by plans, not by doubts."
- Stacey Abrams
Course Description
Classroom: Candler Hall 214 (Social distance capacity: 9)
Meeting Time: Wednesdays 4:10-7:10
This course introduces graduate students of public administration to key concepts in data applications, emphasizing both statistical theory and software skills necessary to understand and perform quantitative analysis. This course covers the entire data application/science process--from collecting and cleaning data, to describing data numerically and visually and drawing conclusions using statistical inference. This course also introduces students to more advanced topics, such as forecasting and causal inference.
Course Objectives
This course directly contributes toward the following MPA program competencies at UGA: 1) To participate in the Public Policy Process, 2) To analyze, synthesize, think critically, solve problems, and make decisions, and 3) Communicate with a diverse workforce and citizenry. By the conclusion of this course, students are expected to be able to:
Analyze policy alternatives using quantitative tools to evaluate decisions and explain potential ramifications for diverse constituencies
Use various methods and analytical tools to analyze and interpret data to provide effective reasoning for decision making and policy creation
Concisely inform the public and other stakeholders of decision and initiatives through the presentation of data and research findings
Produce policy papers involving the synthesis of information, evaluation, and analysis of critical questions or problems currently facing the field of public administration and policy
Execute specific strategies to enhance equity within and representativeness of the public workforce to ensure all people with a government's jurisdiction are well served
Required Course Materials
There is a wealth of free material teaching statistics and statistical software. All required materials for this course are free. There is no textbook for this course. The instructor will provide readings via eLC. Students who plan to use their own computers will have to download the following software:
Students are expected to attend class in-person or online. Both modes will be offered every class meeting, if necessary, and will provide as similar of an experience as technology and my ability allows. If our classroom cannot accommodate all students while socially distancing, in-person attendance will rotate according to a schedule that I will finalize and share at the beginning of the semester. It is your choice whether to attend in-person a class session to which you are invited. Following Thanksgiving Break, there are no in-person class sessions.
Most weekly reading assignments will involve an R Chapter. Each R Chapter provides instructions on how to apply concepts and skills covered that week in R, then asks students to answer a few practice questions. R Chapters will be graded pass/fail based on whether you submit your answers prior to the class period to which the R Chapter was assigned. Sample answers will become available via eLC immediately after you submit your answers.
Most class meeting will include a lab component that covers an applied skill in R. Your instructor will provide instructions, prompts for you to practice the skill, and assistance when needing to troubleshoot. R Labs will be graded pass/fail based on whether you attend class and participate in the R Lab, then submit your work on eLC by the end of the class meeting.
Students are expected to complete three problem sets throughout the semester. Problem sets will include a combination of conceptual and applied questions that require students to use R. Up to three students may work together on a problem set. Problem sets will be graded numerically.
Students are expected to complete a midterm and final exam during the semester. The exams will focus entirely on concepts covered in the course, not use of R. The exams will evaluate students on their understanding of theory and correct practices regarding data description and inference, as well as their ability to interpret and communicate statistical information and make decisions.
Students of this course receive a free account to DataCamp. DataCamp contains numerous interactive exercises that can help you build conceptual understanding of statistics and skills in R. The course schedule provides a list of DataCamp chapters that are relevant to the topics covered each week. The DataCamp chapters are optional. However, if you complete all DataCamp chapters, you will receive bonus points for your final grade.
As a University of Georgia student, you have agreed to abide by the University’s academic honesty policy, “A Culture of Honesty,” and the Student Honor Code. All academic work must meet the standards described in “A Culture of Honesty” found at: http://honesty.uga.edu/. The Academic Honesty Policy can be found at: https://honesty.uga.edu/Academic-Honesty-Policy/
Students who seek special accommodations due to a disability should contact me during the first week of the semester or as soon as the need for the accommodation is discovered. I will work with the Disability Resource Center (706-542-8719, http://drc.uga.edu/) to provide appropriate accommodations.